Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Estimation from Incomplete Measurements.
Tian TongCong MaAshley Prater-BennetteErin TrippYuejie ChiPublished in: CoRR (2021)
Keyphrases
- low rank
- convex optimization
- trace norm
- missing data
- high order
- low rank matrices
- robust principal component analysis
- frobenius norm
- tensor decomposition
- low rank matrix recovery
- rank minimization
- low rank matrix
- linear combination
- matrix factorization
- low rank and sparse
- matrix completion
- regularized regression
- semi supervised
- singular value decomposition
- high dimensional data
- nuclear norm
- matrix decomposition
- kernel matrix
- data sets
- total variation
- higher order
- missing values
- machine learning
- minimization problems
- singular values
- incomplete data
- pairwise
- convex relaxation